R Notebook

library(openprescribingR)
library(tidyverse)

Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
Loading tidyverse: dplyr
package ‘purrr’ was built under R version 3.4.1package ‘dplyr’ was built under R version 3.4.1Conflicts with tidy packages ------------------------------
filter(): dplyr, stats
lag():    dplyr, stats

library(leaflet)
library(sf)

package ‘sf’ was built under R version 3.4.1Linking to GEOS 3.6.1, GDAL 2.1.3, proj.4 4.9.3

library(stringr)
plot2017 <- function(argument)
{
  ccggeom <- sf::st_read("https://openprescribing.net/api/1.0/org_location/?org_type=ccg") %>%
    dplyr::rename(row_name = name) %>%
    dplyr::select(-ons_code, -org_type)
  dataframe <- dplyr::full_join(
    (openprescribingR::list_size() %>%
       dplyr::select(-row_id)),
    (openprescribingR::spending_by_CCG(chemical_section_or_presentation_code =
                       argument)),
    by = c("row_name", "date")) %>%
    dplyr::mutate(costperperson = actual_cost/total_list_size) %>%
    dplyr::full_join(ccggeom, by="row_name") %>%
    sf::st_as_sf() %>%
    dplyr::mutate(label = stringr::str_c(row_name, " £", format(round(costperperson, 2), nsmall = 2)))
  daterange <- dplyr::filter(dataframe, date=="2017-01-01"|date=="2017-02-01"|date=="2017-03-01"|date=="2017-04-01"|date=="2017-05-01")$costperperson
  pal <- leaflet::colorNumeric(palette = "magma",
                      domain = daterange)
  leaflet::leaflet(data=dataframe) %>%
    leaflet::setView(-1.341739, 53.104565, zoom = 6) %>%
    leaflet::addTiles()  %>%
    leaflet::addPolygons(
      data = dplyr::filter(dataframe, date=="2017-05-01"),
      weight = 2,
      label = dplyr::filter(dataframe,
                            date=="2017-05-01")$label,
      fillOpacity =0.8,
      color = ~pal(costperperson),
      group = "May",
      highlightOptions = highlightOptions(color = "black",
                                          weight = 2)) %>%
    leaflet::addPolygons(
      data = dplyr::filter(dataframe, date=="2017-04-01"),
      weight = 2,
      label = dplyr::filter(dataframe,
                            date=="2017-04-01")$label,
      fillOpacity =0.8,
      color = ~pal(costperperson),
      group = "April",
      highlightOptions = highlightOptions(color = "black",
                                          weight = 2)) %>%
    leaflet::addPolygons(
      data = dplyr::filter(dataframe, date=="2017-03-01"),
      weight = 2,
      label = dplyr::filter(dataframe,
                            date=="2017-03-01")$label,
      fillOpacity =0.8,
      color = ~pal(costperperson),
      group = "March",
      highlightOptions = highlightOptions(color = "black",
                                          weight = 2)) %>%
    leaflet::addPolygons(
      data = dplyr::filter(dataframe, date=="2017-02-01"),
      weight = 2,
      label = dplyr::filter(dataframe,
                            date=="2017-02-01")$label,
      fillOpacity =0.8,
      color = ~pal(costperperson),
      group = "February",
      highlightOptions = highlightOptions(color = "black",
                                          weight = 2)) %>%
    leaflet::addPolygons(
      data = dplyr::filter(dataframe, date=="2017-01-01"),
      weight = 2,
      label = dplyr::filter(dataframe,
                            date=="2017-01-01")$label,
      fillOpacity =0.8,
      color = ~pal(costperperson),
      group = "January",
      highlightOptions = highlightOptions(color = "black",
                                          weight = 2)) %>%
    leaflet::addLegend("bottomleft", pal = pal, values = daterange,
              title = stringr::str_c(argument,
                                     " Items cost per person on CCG list"),
              labFormat = labelFormat(prefix = "£"),
              opacity = 1
    ) %>%
    leaflet::addLayersControl(
      baseGroups = c("May", "April", "March",
                     "February", "January", "Nothing"),
      options = layersControlOptions(collapsed = TRUE)
    )
}
plot2017("7.4.5")

Reading layer `OGRGeoJSON' from data source `https://openprescribing.net/api/1.0/org_location/?org_type=ccg' using driver `GeoJSON'
Simple feature collection with 207 features and 3 fields (with 1 geometry empty)
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -6.418591 ymin: 49.86467 xmax: 1.762979 ymax: 55.81111
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

Column `row_name` joining factors with different levels, coercing to character vectorColumn `date` joining factors with different levels, coercing to character vectorColumn `row_name` joining character vector and factor, coercing into character vector

openprescribingRplots::plot2017("7.4.1")

Reading layer `OGRGeoJSON' from data source `https://openprescribing.net/api/1.0/org_location/?org_type=ccg' using driver `GeoJSON'
Simple feature collection with 207 features and 3 fields (with 1 geometry empty)
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -6.418591 ymin: 49.86467 xmax: 1.762979 ymax: 55.81111
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

Column `row_name` joining factors with different levels, coercing to character vectorColumn `date` joining factors with different levels, coercing to character vectorColumn `row_name` joining character vector and factor, coercing into character vector